Top vector databases for AI search

Adds vector search to PostgreSQL; often the simplest pick if your app already uses Postgres.

Best for Apps already on PostgreSQL that want vector search without another databaseYou give up Outside tests found slow index builds and mixed search speed

Embedded vector database that runs inside your app on local files; good for local and multimodal apps.

Best for Local apps with images or other mixed data stored on disk or S3You give up Scaled poorly across 8 threads in the outside test; runs inside your app, not as a server

Fast open-source vector server with strong filtering; turn on an API key, as it starts with no login.

Best for Production search that leans on filters, with a small memory footprintYou give up Starts with no login and with telemetry on; slowest response times varied in testing

Meta's search library, not a database: no server or storage, but fast in-memory search inside your code.

Best for Building your own search on the library many vector databases use underneathYou give up You handle storage, servers and updates yourself; not scored yet

Open-source vector database with built-in keyword plus vector (hybrid) search and model plug-ins.

Best for Apps that need keyword and meaning-based search in one queryYou give up The only outside test found used a version 13 releases old; paid cloud from $45 a month

Easiest pip-install vector store for prototypes and small local apps; releases have slowed since May.

Best for First prototypes of search over your own documentsYou give up No release since May 2026 and no recent outside test; the Team cloud plan is $250 a month

Paid hosted vector search service with nothing to run yourself; $16/mo minimum and no free plan.

Best for Teams that will pay to skip running any servers for vector searchYou give up No free plan; the Scale plan costs at least $256 a month; not scored yet

Fully managed paid vector database with a free 2 GB Starter plan; nothing to host yourself.

Best for Small projects that fit in 2 GB and want no servers to manageYou give up Only recent test was by a vendor built on Qdrant; Standard is $50 a month minimum

Scale-out open-source vector database for very large collections; heavier to run than the others.

Best for Very large collections, up to billions of vectors, spread across machinesYou give up Heavier to run than Qdrant or pgvector; login is off by default; not scored yet

Vector search inside MongoDB Atlas; a sensible pick if your app data already lives in MongoDB.

Best for Adding meaning-based search to MongoDB apps without a second databaseYou give up The free M0 cluster is for testing only; dedicated clusters start at $56.94 a month

Search engine that does keyword and vector search together; fits teams already running Elastic.

Best for Adding vector search to an Elastic setup you already runYou give up Three licence options, with some parts under Elastic License only; not scored yet